Promoting Social Participation and Recovery Using Virtual Reality–Based Interventions Among People With Mental Health and Substance Use Disorders: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: People with mental health disorders (MHDs) and substance use disorders (SUDs) are a highly vulnerable group, particularly affected by social exclusion, marginalization, and disconnectedness. Virtual reality technology holds a potential for simulating social environments and interactions to mitigate the social barriers and marginalization faced by people recovering from MHDs and SUDs. However, it is still unclear how we can harness the greater ecological validity of virtual reality-based interventions targeting social and functional impairments in individuals with MHDs and SUDs. OBJECTIVE: The aim of this paper was to explore how service providers in community-based MHD and SUD health care services perceive the barriers to social participation among adults recovering from MHDs and SUDs to provide a broader understanding of how learning experiences can be modeled to promote social participation in virtual reality environments. METHODS: Two semistructured, open-ended, and dual-moderator focus group interviews were conducted with participants representing different community-based MHD and SUD health care services. Service providers were recruited from their MHD and SUD services in our collaborating municipality in Eastern Norway. We recruited the first participant group at a municipal MHD and SUD assisted living facility for service users with ongoing excessive substance use and severe social dysfunctionality. We recruited the second participant group at a community-based follow-up care service aimed at clients with a broad range of MHDs and SUDs and various levels of social functioning. The qualitative data extracted in the interviews were analyzed, using reflexive thematic analysis. RESULTS: The analysis of the service providers' perceptions of the barriers to social participation among clients with MHDs and SUDs revealed the following five main themes: challenging or lacking social connections, impaired cognitive functions, negative self-perception, impaired personal functioning, and insufficient social security. The barriers identified are interrelated in a cluster of cognitive, socioemotional, and functional impairments, leading to a severe and diverse complex of barriers to social participation. CONCLUSIONS: Social participation relies on people's capability to use their present social opportunities. Promoting basic human functioning is key to promoting social participation among people with MHDs and SUDs. The findings in this study indicate a need to address cognitive functioning, socioemotional learning, instrumental skills, and complex social functions to meet the complexity and diversity of the identified barriers to social functioning in our target group. Virtual reality-based interventions for promoting social participation should be sequenced into distinct scenarios dedicated to specific learning goals to build complex learning in a step-by-step process based on successively more complex levels of human and social functioning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".